The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

Two decades of array signal processing research: the parametric approach

This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a "primary" input containing the corrupted signal and a "reference" input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. Wiener solutions are developed to describe asymptotic adaptive performance and output signal-to-noise ratio for stationary stochastic inputs, including single and multiple reference inputs. These solutions show that when the reference input is free of signal and certain other conditions are met noise in the primary input can be essentiany eliminated without signal distortion. It is further shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution. Experimental results are presented that illustrate the usefulness of the adaptive noise cancelling technique in a variety of practical applications. These applications include the cancelling of various forms of periodic interference in electrocardiography, the cancelling of periodic interference in speech signals, and the cancelling of broad-band interference in the side-lobes of an antenna array. In further experiments it is shown that a sine wave and Gaussian noise can be separated by using a reference input that is a delayed version of the primary input. Suggested applications include the elimination of tape hum or turntable rumble during the playback of recorded broad-band signals and the automatic detection of very-low-level periodic signals masked by broad-band noise.

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Adaptive noise cancelling: Principles and applications

An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included. Several beamformer implementations are briefly described. >

Beamforming: a versatile approach to spatial filtering

Phased Arrays in Radar and Communication Systems. Pattern Characteristics and Synthesis of Linear and Planar Arrays. Patterns of Nonplanar Arrays. Elements, Transmission Lines, and Feed Architectures for Phased Arrays. Summary of Element Pattern and Mutual Impedance Effects. Array Error Effects. Special Array Feeds for Limited Field of View and Wideband Arrays.

Phased Array Antenna Handbook

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.

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Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

A method for adaptively optimizing the signal-to-noise ratio of an array antenna is presented. Optimum element weights are derived for a prescribed environment and a given signal direction. The derivation is extended to the optimization of a "generalized" signal-to-noise ratio which permits specification of preferred weights for the normal quiescent environment. The relation of the adaptive array to sidelobe cancellation is shown, and a real-time adaptive implementation is discussed. For illustration, the performance of an adaptive linear array is presented for various jammer configurations.

Adaptive arrays

Initial applications of adaptive array theory to the radar sidelobe jamming problem ignored the problem of incidental cancellation of the desired signal returns. In more recent applications, longer transmitted waveforms have combined with returns from extended clutter and/or strong targets to create a more serious signal cancellation problem. There are several ways in which the adaptive processor can be constrained from responding to desired main lobe target returns while maintaining good cancellation of interference in the sidelobes. This paper examines the major techniques for constraining the response of the adaptive processor, including methods of controlling the response of the array in the absence of external interference. Time domain and frequency domain techniques are discussed. The majority of the discussion is devoted to angle domain techniques such as pilot signals, preadaption spacial filtering, and control loop spatial filtering. Analysis is presented showing the relationship between these techniques. Finally, examples are given showing the effects of these constraints as well as control of the quiescent array pattern.

S. Applebaum

Papers

Adaptive arrays

Adaptive arrays with main beam constraints